In [1]:
import pandas as pd
import numpy as np

collision_data = pd.read_csv("processed_data.csv")

C:\Users\huang\AppData\Local\Temp\ipykernel_22196\4182500787.py:4: DtypeWarning: Columns (3,29,30) have mixed types. Specify dtype option on import or set low_memory=False.
  collision_data = pd.read_csv("processed_data.csv")
In [4]:
import pandas as pd
import numpy as np
import plotly.express as px

heat_map_data = collision_data[['LATITUDE', 'LONGITUDE','COLLISION_ID']]
heat_map_data = heat_map_data.dropna()

heatmap_counts = heat_map_data.groupby(['LATITUDE', 'LONGITUDE']).size().reset_index(name="Count")

fig = px.density_mapbox(heatmap_counts, lat='LATITUDE', lon='LONGITUDE', z='Count', mapbox_style="stamen-terrain",  color_continuous_scale= 'reds')

fig.update_layout(title_text="Collision Frequency Heatmap")
fig.show()
In [6]:
collision_data['CRASH DATE'] = pd.to_datetime(collision_data['CRASH DATE'])
collision_data['YEAR'] = collision_data['CRASH DATE'].dt.year

heat_map_data = collision_data[['LATITUDE', 'LONGITUDE','COLLISION_ID','YEAR']]
heat_map_data = heat_map_data.dropna()

heatmap_counts = heat_map_data.groupby(['LATITUDE', 'LONGITUDE','YEAR']).size().reset_index(name="Count")

heatmap_2014 = heatmap_counts[(heatmap_counts['YEAR'] == 2014)]
heatmap_2018 = heatmap_counts[(heatmap_counts['YEAR'] == 2018)]
heatmap_2022 = heatmap_counts[(heatmap_counts['YEAR'] == 2022)]

fig1 = px.density_mapbox(heatmap_2014, lat='LATITUDE', lon='LONGITUDE', z='Count', mapbox_style="stamen-terrain",  color_continuous_scale= 'reds')
fig2 = px.density_mapbox(heatmap_2018, lat='LATITUDE', lon='LONGITUDE', z='Count', mapbox_style="stamen-terrain",  color_continuous_scale= 'reds')
fig3 = px.density_mapbox(heatmap_2022, lat='LATITUDE', lon='LONGITUDE', z='Count', mapbox_style="stamen-terrain",  color_continuous_scale= 'reds')


fig1.update_layout(title_text="2014 Collision Frequency Heatmap")
fig2.update_layout(title_text="2018 Collision Frequency Heatmap")
fig3.update_layout(title_text="2022 Collision Frequency Heatmap")

fig1.show()
fig2.show()
fig3.show()
In [ ]: